Articles | Volume 9, issue 8
https://doi.org/10.5194/amt-9-4123-2016
https://doi.org/10.5194/amt-9-4123-2016
Research article
 | 
29 Aug 2016
Research article |  | 29 Aug 2016

Errors in radial velocity variance from Doppler wind lidar

H. Wang, R. J. Barthelmie, P. Doubrawa, and S. C. Pryor

Related authors

Lidar arc scan uncertainty reduction through scanning geometry optimization
Hui Wang, Rebecca J. Barthelmie, Sara C. Pryor, and Gareth. Brown
Atmos. Meas. Tech., 9, 1653–1669, https://doi.org/10.5194/amt-9-1653-2016,https://doi.org/10.5194/amt-9-1653-2016, 2016

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Improving solution availability and temporal consistency of an optimal-estimation physical retrieval for ground-based thermodynamic boundary layer profiling
Bianca Adler, David D. Turner, Laura Bianco, Irina V. Djalalova, Timothy Myers, and James M. Wilczak
Atmos. Meas. Tech., 17, 6603–6624, https://doi.org/10.5194/amt-17-6603-2024,https://doi.org/10.5194/amt-17-6603-2024, 2024
Short summary
An improved geolocation methodology for spaceborne radar and lidar systems
Bernat Puigdomènech Treserras and Pavlos Kollias
Atmos. Meas. Tech., 17, 6301–6314, https://doi.org/10.5194/amt-17-6301-2024,https://doi.org/10.5194/amt-17-6301-2024, 2024
Short summary
Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products
Andreas Walbröl, Hannes J. Griesche, Mario Mech, Susanne Crewell, and Kerstin Ebell
Atmos. Meas. Tech., 17, 6223–6245, https://doi.org/10.5194/amt-17-6223-2024,https://doi.org/10.5194/amt-17-6223-2024, 2024
Short summary
HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution
Giulia Roccetti, Luca Bugliaro, Felix Gödde, Claudia Emde, Ulrich Hamann, Mihail Manev, Michael Fritz Sterzik, and Cedric Wehrum
Atmos. Meas. Tech., 17, 6025–6046, https://doi.org/10.5194/amt-17-6025-2024,https://doi.org/10.5194/amt-17-6025-2024, 2024
Short summary
Global-scale gravity wave analysis methodology for the ESA Earth Explorer 11 candidate CAIRT
Sebastian Rhode, Peter Preusse, Jörn Ungermann, Inna Polichtchouk, Kaoru Sato, Shingo Watanabe, Manfred Ern, Karlheinz Nogai, Björn-Martin Sinnhuber, and Martin Riese
Atmos. Meas. Tech., 17, 5785–5819, https://doi.org/10.5194/amt-17-5785-2024,https://doi.org/10.5194/amt-17-5785-2024, 2024
Short summary

Cited articles

Banta, R. M., Pichugina, Y. L., Kelley, N. D., Hardesty, R. M., and Brewer, W. A.: Wind energy meteorology: Insight into wind properties in the turbine-rotor layer of the atmosphere from high-resolution Doppler lidar, B. Am. Meteorol. Soc., 94, 883–902, https://doi.org/10.1175/BAMS-D-11-00057.1, 2013.
Barthelmie, R. J., Wang, H., Doubrawa, P., Giroux, G., and Pryor, S. C.: Effects of an escarpment on flow parameters of relevance to wind turbines, Wind Energy, https://doi.org/10.1002/we.1980, online first, 2016.
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M.: Time Series Analysis: Forecasting and Control, John Wiley & Sons, 712 pp., 2015.
Branlard, E., Pedersen, A. T., Mann, J., Angelou, N., Fischer, A., Mikkelsen, T., Harris, M., Slinger, C., and Montes, B. F.: Retrieving wind statistics from average spectrum of continuous-wave lidar, Atmos. Meas. Tech., 6, 1673–1683, https://doi.org/10.5194/amt-6-1673-2013, 2013.
Burton, T., Sharpe, D., Jenkins, N., and Bossanyi, E.: Wind energy handbook, John Wiley & Sons, 780 pp., 2011.
Download
Short summary
This paper investigates how long a sampling duration of lidar measurements should be in order to accurately estimate radial velocity variance to obtain turbulence statistics. Using observations and statistical simulations, it is demonstrated that large probe volumes in lidar measurements increase the autocorrelation values, and consequently the uncertainty in radial velocity variance estimates. It is further shown that the random error can exceed 10 % for 30–60 min sampling duration.